{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7dcafcc7-16b9-49a6-9dc6-08dd30c96180",
   "metadata": {},
   "source": [
    "# 密集连接网络拟合cos函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dadf1d26-c3c7-43e5-a8a9-07595be5dd35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, PReLU, Flatten, Softmax\n",
    "from keras.models import Sequential\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd7339d8-72d8-436b-b5fc-f5e47558ea74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 11245354123988559142\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84eddf2b-2b26-4a3f-9c5c-270a2db04f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,1,10000)\n",
    "y = (np.cos((x*2-1)*np.pi)+1)/2\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f84facf3-5bee-4940-9e12-f12f90c2be6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 0.9999999753210537)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.min(),x.max(),y.min(),y.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34645e8e-e817-4e7b-867d-a17344b06117",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(20, activation='relu',input_dim=1))\n",
    "model.add(Dense(40, activation='relu'))\n",
    "# model.add(Dense(10, activation='relu',input_dim=1))\n",
    "model.add(Dense(5, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='mse', optimizer='sgd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a383a58-0507-4ae9-952e-7d8339db7b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载现有模型\n",
    "model = keras.models.load_model('cos.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cdc47e1-3a67-42ec-abfa-651c0491ec94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_8 (Dense)              (None, 20)                40        \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 40)                840       \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 5)                 205       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 1,091\n",
      "Trainable params: 1,091\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ef28e04-beb6-4cec-a642-f5aa9bb71924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "150/150 [==============================] - 0s 3ms/step - loss: 2.9158e-04 - val_loss: 2.9281e-04\n",
      "Epoch 2/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9141e-04 - val_loss: 2.9235e-04\n",
      "Epoch 3/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9147e-04 - val_loss: 2.9268e-04\n",
      "Epoch 4/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9148e-04 - val_loss: 2.9235e-04\n",
      "Epoch 5/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9148e-04 - val_loss: 2.9244e-04\n",
      "Epoch 6/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9145e-04 - val_loss: 2.9234e-04\n",
      "Epoch 7/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9136e-04 - val_loss: 2.9266e-04\n",
      "Epoch 8/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9148e-04 - val_loss: 2.9263e-04\n",
      "Epoch 9/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9129e-04 - val_loss: 2.9256e-04\n",
      "Epoch 10/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9102e-04 - val_loss: 2.9287e-04\n",
      "Epoch 11/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9136e-04 - val_loss: 2.9228e-04\n",
      "Epoch 12/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9135e-04 - val_loss: 2.9241e-04\n",
      "Epoch 13/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9133e-04 - val_loss: 2.9238e-04\n",
      "Epoch 14/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9127e-04 - val_loss: 2.9222e-04\n",
      "Epoch 15/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9118e-04 - val_loss: 2.9234e-04\n",
      "Epoch 16/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9108e-04 - val_loss: 2.9305e-04\n",
      "Epoch 17/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9131e-04 - val_loss: 2.9235e-04\n",
      "Epoch 18/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9118e-04 - val_loss: 2.9225e-04\n",
      "Epoch 19/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9119e-04 - val_loss: 2.9220e-04\n",
      "Epoch 20/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9117e-04 - val_loss: 2.9222e-04\n",
      "Epoch 21/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9107e-04 - val_loss: 2.9227e-04\n",
      "Epoch 22/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9110e-04 - val_loss: 2.9211e-04\n",
      "Epoch 23/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9107e-04 - val_loss: 2.9228e-04\n",
      "Epoch 24/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9108e-04 - val_loss: 2.9233e-04\n",
      "Epoch 25/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9112e-04 - val_loss: 2.9220e-04\n",
      "Epoch 26/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9115e-04 - val_loss: 2.9209e-04\n",
      "Epoch 27/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9115e-04 - val_loss: 2.9205e-04\n",
      "Epoch 28/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9090e-04 - val_loss: 2.9238e-04\n",
      "Epoch 29/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9103e-04 - val_loss: 2.9211e-04\n",
      "Epoch 30/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9119e-04 - val_loss: 2.9206e-04\n",
      "Epoch 31/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9112e-04 - val_loss: 2.9204e-04\n",
      "Epoch 32/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9086e-04 - val_loss: 2.9209e-04\n",
      "Epoch 33/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9096e-04 - val_loss: 2.9199e-04\n",
      "Epoch 34/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9096e-04 - val_loss: 2.9250e-04\n",
      "Epoch 35/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9103e-04 - val_loss: 2.9202e-04\n",
      "Epoch 36/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9086e-04 - val_loss: 2.9219e-04\n",
      "Epoch 37/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9094e-04 - val_loss: 2.9227e-04\n",
      "Epoch 38/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9093e-04 - val_loss: 2.9206e-04\n",
      "Epoch 39/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9090e-04 - val_loss: 2.9198e-04\n",
      "Epoch 40/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9091e-04 - val_loss: 2.9192e-04\n",
      "Epoch 41/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9084e-04 - val_loss: 2.9189e-04\n",
      "Epoch 42/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9086e-04 - val_loss: 2.9190e-04\n",
      "Epoch 43/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9079e-04 - val_loss: 2.9190e-04\n",
      "Epoch 44/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9079e-04 - val_loss: 2.9200e-04\n",
      "Epoch 45/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9069e-04 - val_loss: 2.9183e-04\n",
      "Epoch 46/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9069e-04 - val_loss: 2.9185e-04\n",
      "Epoch 47/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9079e-04 - val_loss: 2.9188e-04\n",
      "Epoch 48/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9076e-04 - val_loss: 2.9221e-04\n",
      "Epoch 49/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9077e-04 - val_loss: 2.9197e-04\n",
      "Epoch 50/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9081e-04 - val_loss: 2.9180e-04\n",
      "Epoch 51/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9078e-04 - val_loss: 2.9184e-04\n",
      "Epoch 52/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9067e-04 - val_loss: 2.9177e-04\n",
      "Epoch 53/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9074e-04 - val_loss: 2.9174e-04\n",
      "Epoch 54/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9064e-04 - val_loss: 2.9231e-04\n",
      "Epoch 55/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9077e-04 - val_loss: 2.9186e-04\n",
      "Epoch 56/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9064e-04 - val_loss: 2.9195e-04\n",
      "Epoch 57/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9071e-04 - val_loss: 2.9175e-04\n",
      "Epoch 58/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9063e-04 - val_loss: 2.9192e-04\n",
      "Epoch 59/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9059e-04 - val_loss: 2.9165e-04\n",
      "Epoch 60/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9065e-04 - val_loss: 2.9191e-04\n",
      "Epoch 61/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9057e-04 - val_loss: 2.9165e-04\n",
      "Epoch 62/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9055e-04 - val_loss: 2.9162e-04\n",
      "Epoch 63/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9056e-04 - val_loss: 2.9162e-04\n",
      "Epoch 64/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9045e-04 - val_loss: 2.9171e-04\n",
      "Epoch 65/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9054e-04 - val_loss: 2.9178e-04\n",
      "Epoch 66/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9039e-04 - val_loss: 2.9199e-04\n",
      "Epoch 67/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9042e-04 - val_loss: 2.9154e-04\n",
      "Epoch 68/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9050e-04 - val_loss: 2.9155e-04\n",
      "Epoch 69/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9040e-04 - val_loss: 2.9155e-04\n",
      "Epoch 70/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9032e-04 - val_loss: 2.9179e-04\n",
      "Epoch 71/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9044e-04 - val_loss: 2.9155e-04\n",
      "Epoch 72/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9041e-04 - val_loss: 2.9174e-04\n",
      "Epoch 73/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9023e-04 - val_loss: 2.9202e-04\n",
      "Epoch 74/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9033e-04 - val_loss: 2.9219e-04\n",
      "Epoch 75/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9037e-04 - val_loss: 2.9143e-04\n",
      "Epoch 76/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9023e-04 - val_loss: 2.9230e-04\n",
      "Epoch 77/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9038e-04 - val_loss: 2.9163e-04\n",
      "Epoch 78/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9025e-04 - val_loss: 2.9170e-04\n",
      "Epoch 79/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9028e-04 - val_loss: 2.9151e-04\n",
      "Epoch 80/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9029e-04 - val_loss: 2.9158e-04\n",
      "Epoch 81/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9035e-04 - val_loss: 2.9134e-04\n",
      "Epoch 82/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9027e-04 - val_loss: 2.9141e-04\n",
      "Epoch 83/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9026e-04 - val_loss: 2.9136e-04\n",
      "Epoch 84/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9017e-04 - val_loss: 2.9131e-04\n",
      "Epoch 85/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9011e-04 - val_loss: 2.9131e-04\n",
      "Epoch 86/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9014e-04 - val_loss: 2.9179e-04\n",
      "Epoch 87/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9021e-04 - val_loss: 2.9139e-04\n",
      "Epoch 88/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9020e-04 - val_loss: 2.9128e-04\n",
      "Epoch 89/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9004e-04 - val_loss: 2.9221e-04\n",
      "Epoch 90/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9022e-04 - val_loss: 2.9133e-04\n",
      "Epoch 91/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9016e-04 - val_loss: 2.9123e-04\n",
      "Epoch 92/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9017e-04 - val_loss: 2.9138e-04\n",
      "Epoch 93/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9008e-04 - val_loss: 2.9121e-04\n",
      "Epoch 94/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9005e-04 - val_loss: 2.9149e-04\n",
      "Epoch 95/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.8991e-04 - val_loss: 2.9129e-04\n",
      "Epoch 96/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9018e-04 - val_loss: 2.9115e-04\n",
      "Epoch 97/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9001e-04 - val_loss: 2.9173e-04\n",
      "Epoch 98/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9007e-04 - val_loss: 2.9121e-04\n",
      "Epoch 99/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9007e-04 - val_loss: 2.9110e-04\n",
      "Epoch 100/100\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 2.9000e-04 - val_loss: 2.9110e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2598740ae20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,batch_size=50,epochs=100,validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf2f6b64-974c-4151-a166-0c23736018ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dcc6173d-2ca0-422a-83be-e286dd768eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "tx = np.linspace(0,1,100)\n",
    "ty = model.predict(tx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e2166a4-1eb3-4f8e-b23d-a88c1dfe444b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sx = np.linspace(-np.pi,np.pi,100)\n",
    "sy = np.cos(sx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "217e1a24-02f5-43a1-910d-2ebf4677789c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=300)\n",
    "plt.title('密集连接网络拟合cos函数')\n",
    "plt.yticks([-1,0,1])\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "       [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$\\pi/2$', r'$\\pi$'])\n",
    "l1, = plt.plot(sx, sy, color='blue')\n",
    "l2, = plt.plot((tx-0.5)*np.pi*2,(ty-0.5)*2,color='red')\n",
    "plt.legend(handles=[l1,l2],labels=['cos','fitting'],loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5b6bed9-1d58-4141-aa28-6b3d95e8fc3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('cos.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bigdata",
   "language": "python",
   "name": "bigdata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
